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Abstract #4450

Super-resolution across RF-encoding and q-space dimensions via physics-driven neural fields for accelerated gSlider diffusion MRI

Atakan Topcu1,2, Congyu Liao3,4, Tolga Çukur1,2, Kawin Setsompop3,4, and Emine Ulku Saritas1,2
1Electrical and Electronics Engineering Department, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Center (UMRAM), Ankara, Turkey, 3Electrical Engineering Department, Stanford University, San Francisco, CA, United States, 4Radiology Department, Stanford University, San Francisco, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, AI/ML Image Reconstruction, Neural Fields, Diffusion MRI, q-Space, Acceleration

Motivation: gSlider, a state-of-the-art diffusion MRI technique, requires extensive sampling of thick-slice RF-encoding and q-space points to resolve fiber structures at the submillimeter level. This results in impractically long scan times.

Goal(s): Our goal is to super-resolve gSlider data in RF-encoding and q-space dimensions to enhance scan efficiency while maintaining high-fidelity diffusion metrics.

Approach: We introduce a novel self-supervised model, sq-QUCCI, that cascades two neural field modules with physics-driven regularization to super-resolve undersampled gSlider acquisitions.

Results: Compared to state-of-the-art baselines, sq-QUCCI achieves superior fidelity in diffusion metrics at 500μm resolution while enabling 7-fold reduction in scan time for gSlider acquisitions.

Impact: sq-QUCCI enables collection of whole-brain high-spatial/angular-resolution, high-SNR diffusion MRI data in a 15-min scan by super-resolving across RF-encoding and q-space dimensions of undersampled gSlider acquisitions, overcoming the efficiency barrier for adoption in clinical settings.

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Keywords